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Background: Portal vein velocity (PVV) has shown a reasonable correlation with the presence of portal hypertension in patients with cirrhosis. This study aims to evaluate the value of PVV for diagnosing clinically significant portal hypertension (CSPH) and predicting the risk of variceal hemorrhage (VH) in patients with hepatitis B virus (HBV)-related cirrhosis. Material and Methods: A cohort of 166 consecutive adult patients with HBV-related cirrhosis was recruited in this retrospective study from two high-volume liver centers in China between April 2015 and April 2017. The performance of PVV and other non-invasive parameters for diagnosing CSPH and predicting the risk of VH was studied. Results: PVV demonstrated the best performance for diagnosing CSPH (defined as an HVPG ≥10 mmHg) in patients with HBV-related cirrhosis among the included non-invasive predictors with the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity of 0.745, 50%, and 93.5%, respectively. Other non-invasive markers, including APRI, AAR, LS, FIB-4, and diameter of the portal vein, did not show sufficient performance with the AUCs of 0.565, 0.560, 0.544, 0.529, and 0.474, respectively. With regard to predicting the risk of VH (defined as an HVPG ≥12 mmHg), PPV also exhibited a moderate performance with an AUC of 0.762, which was superior to that of the aforementioned markers. By using two cutoff values of PVV to rule-out (11.65 cm/s) and rule-in (20.20 cm/s) CSPH, 30 (33.7%) patients showed definite results categories, with 23 (76.7%) patients were well classified and 7 (23.3%) were misclassified. Fifty-nine (66.3%) patients were with indeterminate results. By using PVV values of 13.10 cm/s and 21.40 cm/s to rule-out and rule-in HVPG ≥ 12mmHg, 34 (38.2%) patients has definite results, among whom 26 (76.5%) were well classified and 8 (23.5%) were misclassified. And 55 (61.8%) patients required further evaluation. Conclusion: PPV is insufficient to serve as a non-invasive parameter for identifying CSPH and predicting the risk of VH in patients with HBV-related cirrhosis.

Hepatic venous pressure gradient (HVPG) is the gold standard for portal pressure in cirrhosis, but most previous studies focused on the diagnostic value of clinically significant portal hypertension (CSPH) based on the correlation between liver stiffness (LS) and HVPG in hepatitis C virus (HCV) patients and alcoholic liver. Therefore, it is necessary to clarify the diagnostic value of LS for CSPH and the correlation with HVPG in hepatitis B virus (HBV) patients.A total of 137 patients from the Fifth Medical Center of PLA General Hospital were divided into HBV group and non-HBV group according to etiology. Correlation analysis and ROC were used to analyze the correlation between LS and HVPG and the diagnostic value of CSPH.There was a good correlation between LS and HVPG in the total cohort and non-HBV cohort (r = 0.398, P 0.001; r = 0.575, P 0.001, respectively). However, the correlation between LS and HVPG was acceptable in the HBV cohort (r = 0.316, P = 0.002). When adjustment for age, MELD score, and INR, the result was still the same. Similar results were observed in the prediction for CSPH. LS showed good diagnostic value for CSPH in the total cohort and non-HBV cohort (AUC = 0.732, AUC = 0.829, respectively). However, it performed poorly in the HBV cohort (AUC = 0.689).The etiology of HBV might affect the diagnostic performance of LS for predicting CSPH.

Rapid diagnostics of infectious diseases and accurate identification of their causative pathogens play a crucial role in disease prevention, monitoring, and treatment. Conventional molecular detection of infectious pathogens requires expensive equipment and well-trained personnel, thus limiting its use in centralized clinical laboratories. To address this challenge, a portable smartphone-based quantitative molecular detection platform, termed “smart connected pathogen tracer” (SCPT), has been developed for pathogen monitoring and disease surveillance. The platform takes advantage of synergistically enhanced colorimetric loop-mediated isothermal amplification (LAMP) assay and smartphone-based color analysis, enabling simple, rapid and reliable nucleic acid quantification without need for expensive fluorescence detection equipment. The SCPT platform has been successfully applied to quantitatively detect: i) HPV DNA in saliva and clinical vaginal swab samples, and ii) HIV RNA in plasma samples with comparable sensitivity to state-of-art machine. It has also been demonstrated for disease spatiotemporal mapping and pathogen tracking by wireless connection and web-based surveillance. Such simple, cost-affordable, portable molecular detection platform has great potential for on-site early disease detection, remote healthcare monitoring, and epidemic surveillance.

The COVID-19 pandemic, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has become a public health emergency and widely spread around the world. Rapid, accurate and early diagnosis of COVID-19 infection plays a crucial role in breaking this pandemic. However, the detection accuracy is limited for current single-gene diagnosis of SARS-CoV-2. Herein, we develop an autonomous lab-on-paper platform for multiplex gene diagnosis of SARS-CoV-2 by combining reverse transcription recombinase polymerase amplification (RT-RPA) and CRISPR-Cas12a detection. The autonomous lab-on-paper is capable of simultaneously detecting nucleoprotein (N) gene and spike (S) gene of SARS-CoV-2 virus as well as Human housekeeping RNAse P gene (an internal control) in a single clinical sample. With the developed platform, 10(2) copies viral RNA per test can be detected within one hour. Also, the lab-on-paper platform has been used to detect 21 swab clinical samples and obtains a comparable performance to the conventional RT-PCR method. Thus, the developed lab-on-paper platform holds great potential for rapid, sensitive, reliable, multiple molecular diagnostics of COVID-19 and other infectious diseases in resource-limited settings.

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (aHVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools. The final aHVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.

Electrocardiogram (ECG) and phonocardiogram (PCG) are both noninvasive and convenient tools that can capture abnormal heart states caused by coronary artery disease (CAD). However, it is very challenging to detect CAD relying on ECG or PCG alone due to low diagnostic sensitivity. Recently, several studies have attempted to combine ECG and PCG signals for diagnosing heart abnormalities, but only conventional manual features have been used. Considering the strong feature extraction capabilities of deep learning, this paper develops a multi-input convolutional neural network (CNN) framework that integrates time, frequency, and time-frequency domain deep features of ECG and PCG for CAD detection. Simultaneously recorded ECG and PCG signals from 195 subjects are used. The proposed framework consists of 1-D and 2-D CNN models and uses signals, spectrum images, and time-frequency images of ECG and PCG as inputs. The framework combining multi-domain deep features of two-modal signals is very effective in classifying non-CAD and CAD subjects, achieving an accuracy, sensitivity, and specificity of 96.51%, 99.37%, and 90.08%, respectively. The comparison with existing studies demonstrates that our method is very competitive in CAD detection. The proposed approach is very promising in assisting the real-world CAD diagnosis, especially under general medical conditions.

DNA detection plays an important role in the rapid screening of cancers and early diagnosis of infectious diseases. Here, we developed a simple, versatile, electric field-enhanced (EFE), electrochemical CRISPR biosensor to detect DNA targets in a homogeneous solution phase. To improve the detection sensitivity, we applied a pulsed electric field to enrich nucleic acids on the electrode surface. The EFE electrochemical CRISPR biosensor takes advantage of the diffusivity difference between electrochemical oligonucleotide probes and CRISPR-cleaved probes toward a negatively charged working electrode, enabling simple and sensitive electrochemical detection of DNA without the need for complicated immobilization processing of electrochemical probes. Our developed CRISPR biosensor directly detects unamplified human papillomavirus-16 (HPV-16) DNA with a sensitivity of 1 pM. Further, the EFE electrochemical CRISPR biosensor coupled with recombinase polymerase amplification (RPA) successfully detects HPV-16 DNA in clinical samples. Thus, the EFE electrochemical CRISPR biosensor provides a simple, robust, and sensitive detection method for nucleic acid-based molecular diagnostics.

Rapid and efficient biological sample preparation and pretreatment are crucial for highly sensitive, reliable and reproducible molecular detection of infectious diseases. Herein, we report a self-powered, integrated sample concentrator (SPISC) for rapid plasma separation, pathogen lysis, nucleic acid trapping and enrichment at the point of care. The proposed sample concentrator uses a combination of gravitational sedimentation of blood cells and capillary force for rapid, self-powered plasma separation. The pathogens (e.g., HIV virus) in separated plasma were directly lysed and pathogen nucleic acid was enriched by an integrated, flow-through FTA® membrane in the concentrator, enabling highly efficient nucleic acid preparation. The FTA® membrane of the SPISC is easy to store and transport at room temperature without need for uninterrupted cold chain, which is crucial for point of care sampling in resource-limited settings. The platform has been successfully applied to detect HIV virus in blood samples. Our experiments show that the sample concentrator can achieve a plasma separation efficiency as high as 95% and a detection sensitivity as low as 10 copies per 200 μL blood (∼100 copies per mL plasma) with variability less than 7%. The sample concentrator described is fully compatible with downstream nucleic acid detection and has great potential for early diagnostics, monitoring and management of infectious diseases at the point of care.

OBJECTIVES A liver stiffness × spleen size/platelet count score (LSPS) model which can rule out high-risk varices and identify high likelihood of clinically significant portal hypertension in patients with compensated cirrhosis has been endorsed by American Association for the Study of Liver Diseases in the 2016 practice guidance on portal hypertension bleeding. This study aims to evaluate the accuracy of LSPS model assessed by ultrasound in well characterized patients with compensated advanced chronic liver disease. METHODS Eligible patients with compensated advanced chronic liver disease were retrospectively enrolled between January 2017 and March 2018, who had undergone routine clinical and laboratory tests, liver stiffness measurement, ultrasound examination, and computed tomography scanning. Spleen sizes were evaluated by ultrasound and computed tomography reconstructed model, respectively. The correlation and agreement of spleen size and LSPS derived from ultrasound and computed tomography imaging modality were compared. RESULTS A total of 158 patients were included and analyzed. Spleen size showed a moderate correlation (R = 0.649, P

Nucleic acid amplification tests have been widely used in clinical diagnostics, food safety monitoring, and molecular biology. Loop-mediated isothermal amplification (LAMP) is a prevailing nucleic acid isothermal amplification method. It has become a powerful alternative to conventional polymerase chain reaction (PCR) for pathogen detection because of its simplicity, rapidity, and high sensitivity. However, the current LAMP methods, especially the LAMP with two loop primers, suffer from undesired nonspecific amplification with strong background signals due to the increasing target sites. This nonspecific amplification substantially reduced the reliability of the LAMP and limited its applications in clinical diagnostics. Here, we report a “dual-priming” (“self-priming” and “pairing-priming”) isothermal amplification (DAMP) assay for rapid nucleic acid detection with ultralow nonspecific signals. This method takes advantage of the “dual-priming” strand extension strategy by adding two pairing-competition primers and designing unique inner primers, enabling highly sensitive and specific molecular detection. As an application demonstration, DAMP assay was used to detect HIV-1 DNA/ RNA and Escherichia coli DNA, showing equal or better sensitivity with shorter detection time compared to conventional LAMP and PCR methods. More importantly, the DAMP assay showed ultralow background signals without false positive signals even after 2-hour incubation. Such simple, reliable, sensitive, and specific DAMP assay can be well suitable for rapid nucleic acid detection as point of care testing, particularly in the resource-limited settings.

A Poincaré plot is a return map that geometrically elucidates the progression of a time-series. It has frequently been used in heart rate variability analyses. However, algorithms for dedicatedly dissecting the shape of this geometrical plot are yet to be established. In this study, we proposed a gridded Poincaré plot by coarse-graining the original graph and using the newly proposed one, defined two novel measures, namely gridded distribution rate (GDR) and gridded distribution entropy (GDE). The GDR essentially represents the percentage of grids with points, while the GDE estimates the Shannon entropy of the grid weight; that is, the number of points in each grid. The performances of the two measures were examined using both theoretical data with known dynamics and experimental short-term RR interval time-series, and they were compared with several existing metrics. Simulation tests demonstrated that both the GDR and GDE could distinguish among different dynamics, while all the compared methods failed. The experimental results further indicated the ability of the GDR and GDE to differentiate healthy young people from healthy aged adults as well as distinguish healthy subjects from patients with coronary artery disease. Our results suggest that the proposed GDR and GDE may better characterize the Poincaré plot in terms of differentiating between varying dynamical regimes, and between human physiological or pathological conditions. Further studies are warranted to establish their feasibility in evaluating cardiovascular functions in clinical practice.

Rationale: Early and accurate detection of disease is crucial for its prevention, identification, and treatment. However, most of disease diagnostics is still limited in clinical laboratories due to the need of complicated instruments and professional personnel. Herein, we reported a smartphone-based synergistically enhanced colorimetric method for molecular diagnostics in our point of care (POC) smart cup platform. Methods: A disposable microfluidic chip was developed for colorimetric loop-mediated isothermal amplification (LAMP) detection of multiple HPV DNA in our POC smart cup platform. The colorimetric detection takes advantage of synergistic effect of PPi4- and H+ ions, two byproducts of LAMP reaction. Color signal of LAMP assay was recorded and analyzed by our custom Android app (dubbed “Hue Analyzer”). Results: Our method not only significantly improves colorimetric readout, but also provides a 10-fold increase in detection sensitivity. It has been successfully applied for HPV-associated cancer screening with spiked saliva and clinical swab samples. Conclusion: The proposed POC diagnostic platform is completely compatible with other nucleic acid biomarkers and has great potential for personalized health monitoring and disease prevention.

Immunotherapy with checkpoint inhibitor programmed cell death 1 (PD-1)/programmed death ligand-1 (PD-L1) antibodies demonstrates improvements in treatment of advanced non–small cell lung cancer. Treatment stratification depends on immunohistochemical PD-L1 measurement of biopsy material, an invasive method that does not account for spatiotemporal heterogeneity. Using a single-domain antibody, NM-01, against PD-L1, radiolabeled site-specifically with 99mTc for SPECT imaging, we aimed to assess the safety, radiation dosimetry, and imaging characteristics of this radiopharmaceutical and correlate tumor uptake with PD-L1 immunohistochemistry results.Methods: Sixteen patients (mean age, 61.7 y; 11 men) with non–small cell lung cancer were recruited. Primary tumor PD-L1 expression was measured by immunohistochemistry. NM-01 was radiolabeled with [99mTc(OH2)3(CO)3]+ complex binding to its C-terminal hexahistidine tag. Administered activity was 3.8–10.4 MBq/kg, corresponding to 100 μg or 400 μg of NM-01. Whole-body planar and thoracic SPECT/CT scans were obtained at 1 and 2 h after injection in all patients, and 5 patients underwent additional imaging at 10 min, 3 h, and 24 h for radiation dosimetry calculations. All patients were monitored for adverse events.Results: No drug-related adverse events occurred in this study. The mean effective dose was 8.84 × 10−3 ± 9.33 × 10−4 mSv/MBq (3.59 ± 0.74 mSv per patient). Tracer uptake was observed in the kidneys, spleen, liver, and bone marrow. SPECT primary tumor–to–blood-pool ratios (T:BP) varied from 1.24 to 2.3 (mean, 1.79) at 1 h and 1.24 to 3.53 (mean, 2.22) at 2 h (P = 0.005). Two-hour primary T:BP ratios correlated with PD-L1 immunohistochemistry results (r = 0.68, P = 0.014). Two-hour T:BP was lower in tumors with ≤1% PD-L1 expression (1.89 vs. 2.49, P = 0.048). Nodal and bone metastases showed tracer uptake. Heterogeneity (>20%) between primary tumor and nodal T:BP was present in 4 of 13 patients.Conclusion: This first-in-human study demonstrates that 99mTc-labeled anti–PD-L1-single-domain antibody SPECT/CT imaging is safe and associated with acceptable dosimetry. Tumor uptake is readily visible against background tissues, particularly at 2 h when the T:BP ratio correlates with PD-L1 immunohistochemistry results.

We described a ribonuclease-dependent cleavable beacon primer, an energy-transfer-tagged oligonucleotide inserted with a ribonucleotide, which can be cleaved by ribonuclease to generate enhanced fluorescence signals and initiate DNA amplification for single nucleotide mutation detection with ultrahigh sensitivity and selectivity.

With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is necessary to develop models and algorithms that can analyze the large amounts of ECG data obtained in real-time. Accurate ECG delineation is key to assisting cardiologists in diagnosing cardiac diseases. The main objective of this study is to design a delineation model based on the encoder-decoder structure to detect different heartbeat waveforms, including P-waves, QRS complexes, T-waves, and No waves (NW), as well as the onset and offset of these waveforms. First, the introduction of a standard dilated convolution module (SDCM) into the encoder path enabled the model to extract more useful ECG signal-informative features. Subsequently, bidirectional long short-term memory (BiLSTM) was added to the encoding structure to obtain numerous temporal features. Moreover, the feature sets of the ECG signals at each level in the encoder path were connected to the decoder part for multi-scale decoding to mitigate the information loss caused by the pooling operation in the encoding process. Finally, the proposed model was trained and tested on both QT and LU databases, and it achieved accurate results compared to other state-of-the-art methods. Regarding the QT database, the average accuracy of ECG waveform classification was 96.90%, and an average classification accuracy of 95.40% was obtained on the LU database. In addition, average F1 values of 99.58% and 97.05% were achieved in the ECG delineation task of the QT and LU databases, respectively. The results show that the proposed ECG_SegNet model has good flexibility and reliability when applied to ECG delineation, and it is a reliable method for analyzing ECG signals in real-time.

As the COVID-19 pandemic continues, people are becoming infected at an alarming rate, individuals are unknowingly spreading disease, and more lives are lost every day. There is an immediate need for a simple, rapid, early and sensitive point-of-care testing for COVID-19 disease. However, current testing approaches do not meet such need. Recently, clustered regularly interspaced short palindromic repeats (CRISPR)-based detection methods have received substantial attention for nucleic acid-based molecular testing due to their simplicity, high sensitivity and specificity. This review explores the various CRISPR-based COVID-19 detection methods and related diagnostic devices. As with any emerging technology, CRISPR/Cas-based nucleic acid testing methods have several challenges that must be overcome for practical applications in clinics and hospitals. More importantly, these detection methods are not limited to COVID-19 but can be applied to detect any type of pathogen, virus, and fungi that may threaten humans, agriculture, and food industries in resource-limited settings. CRISPR/Cas-based detection methods have the potential to become simpler, more reliable, more affordable, and faster in the near future, which is highly important for achieving point-of-care diagnostics.

Fluorescence detection of nucleic acid isothermal amplification utilizing energy-transfer-tagged oligonucleotide probes provides a highly sensitive and specific method for pathogen detection. However, currently available probes suffer from relatively weak fluorescence signals and are not suitable for simple, affordable smartphone-based detection at the point of care. Here, we present a cleavable hairpin beacon (CHB)-enhanced fluorescence detection for isothermal amplification assay. The CHB probe is a single fluorophore-tagged hairpin oligonucleotide with five continuous ribonucleotides which can be cleaved by the ribonuclease to specifically initiate DNA amplification and generate strong fluorescence signals. By coupling with loop-mediated isothermal amplification (LAMP), the CHB probe could detect Borrelia burgdorferi (B. burgdorferi) recA gene with a sensitivity of 100 copies within 25 min and generated stronger specific fluorescence signals which were easily read and analysed by our programmed smartphone. Also, this CHB-enhanced LAMP (CHB-LAMP) assay was successfully demonstrated to detect B. burgdorferi DNA extracted from tick species, showing comparable results to real-time PCR assay. In addition, our CHB probe was compatible with other isothermal amplifications, such as isothermal multiple-self-matching-initiated amplification (IMSA). Therefore, CHB-enhanced fluorescence detection is anticipated to facilitate the development of simple, sensitive smartphone-based point-of-care pathogen diagnostics in resource-limited settings.

Background and aims: Coronavirus disease 2019 (COVID-19) pandemic has become a global health issue. This study aimed to explore the clinical characteristics and CT imaging features of patients with COVID-19 on admission. Methods: Consecutive patients with laboratory-confirmed COVID-19 were retrospectively recruited to this study from January 2020 to March 2020. According to the disease severity status on admission, patients were divided into two groups, the common group, and the severe group. Results: Forty-four patients (F/M 20/24) who were COVID-19 positive were enrolled in this study. The most common onset symptom was fever (90.9%), followed by cough (43.2%). As for the laboratory tests, common findings included increased C reactive protein (47.7%) and erythrocyte sedimentation rate (43.2%) and decreased lymphocyte (34.1%). The frequency of decreased lymphocyte count and increased lactate dehydrogenasewas higher in the severe group (n=14) than in the common group (n=30). About 86% of patients showed typical imaging findings of COVID-19 infection, including ground-glass opacity with ill-defined margins, air bronchogram, interlobular septal thickening, and consolidation. Lesions were mainly located in the peripheral and subpleural regions with diffused distribution and multiple lung lobes were found to be affected. Conclusion: Fever and cough were the most common onset symptoms of COVID-19. Increased C reactive protein and erythrocyte sedimentation rate were the most common laboratory findings. Typical signs of chest CT imaging of COVID-19 included ground-glass opacity with ill-defined margins, air bronchogram, interlobular septal thickening, and consolidation.

Recently, CRISPR-Cas technology has opened a new era of nucleic acid-based molecular diagnostics. However, current CRISPR-Cas-based nucleic acid biosensing has a lack of the quantitative detection ability and typically requires separate manual operations. Herein, we reported a dynamic aqueous multiphase reaction (DAMR) system for simple, sensitive and quantitative one-pot CRISPR-Cas12a based molecular diagnosis by taking advantage of density difference of sucrose concentration. In the DAMR system, recombinase polymerase amplification (RPA) and CRISPR-Cas12a derived fluorescent detection occurred in spatially separated but connected aqueous phases. Our DAMR system was utilized to quantitatively detect human papillomavirus (HPV) 16 and 18 DNAs with sensitivities of 10 and 100 copies within less than one hour. Multiplex detection of HPV16/18 in clinical human swab samples were successfully achieved in the DAMR system using 3D-printed microfluidic device. Furthermore, we demonstrated that target DNA in real human plasma samples can be directly amplified and detected in the DAMR system without complicated sample pre-treatment. As demonstrated, the DAMR system has shown great potential for development of next-generation point-of-care molecular diagnostics.

Objective: Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients. Approach: Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification. Main results: Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively. Significance: The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.

A recent outbreak of novel coronavirus (SARS-CoV-2), the causative agent of COVID-19, has spread rapidly all over the world. Human immunodeficiency virus (HIV) is another deadly virus and causes acquired immunodeficiency syndrome (AIDS). Rapid and early detection of these viruses will facilitate early intervention and reduce disease transmission risk. Here, we present anAll-In-OneDualCRISPR-Cas12a (termed “AIOD-CRISPR”) assay method for simple, rapid, ultrasensitive, one-pot, and visual detection of coronavirus SARS-CoV-2 and HIV virus. In our AIOD CRISPR assay, a pair of crRNAs was introduced to initiate dual CRISPR-Cas12a detection and improve detection sensitivity. The AIOD-CRISPR assay system was successfully utilized to detect nucleic acids (DNA and RNA) of SARS-CoV-2 and HIV with a sensitivity of few copies. Also, it was evaluated by detecting HIV-1 RNA extracted from human plasma samples, achieving a comparable sensitivity with real-time RT-PCR method. Thus, our method has a great potential for developing next-generation point-of-care molecular diagnostics.

Phonocardiogram (PCG) signals reflect the mechanical activity of the heart. Previous studies have reported that PCG signals contain heart murmurs caused by coronary artery disease (CAD). However, the murmurs caused by CAD are very weak and rarely heard by the human ear. In this paper, a novel feature fusion framework is proposed to provide a comprehensive basis for CAD diagnosis. A dataset containing PCG signals of 175 subjects was collected and used. A total of 110 features were extracted from multiple domains, and then reduced and selected. Images obtained by Mel-frequency cepstral coefficients were used as the input for the convolutional neural network for feature learning. Then, the selected features and the deep learning features were fused and fed into a multilayer perceptron for classification. The proposed feature fusion method achieved better classification performance than multi-domain features or deep learning features alone, with accuracy, sensitivity, and specificity of 90.43%, 93.67%, and 83.36%, respectively. A comparison with existing studies demonstrated that the proposed method was a promising noninvasive screening tool for CAD under general medical conditions.

Clinically significant portal hypertension (CSPH) is associated with an incremental risk of esophageal varices and overt clinical decompensations. However, hepatic venous pressure gradient (HVPG) measurement, the gold standard for defining CSPH (HVPG≥10 mm Hg) is invasive and therefore not suitable for routine clinical practice. This study aims to develop and validate a radiomics-based model as a noninvasive method for accurate detection of CSPH in cirrhosis. The prospective multicenter diagnostic trial (CHESS1701, ClinicalTrials.gov identifier: NCT03138915 ) involved 385 patients with cirrhosis from five liver centers in China between August 2016 and September 2017. Patients who had both HVPG measurement and contrast-enhanced CT within 14 days prior to the catheterization were collected. The noninvasive radiomics model, termed rHVPG for CSPH was developed based on CT images in a training cohort consisted of 222 consecutive patients and the diagnostic performance was prospectively assessed in 163 consecutive patients in four external validation cohorts. rHVPG showed a good performance in detection of CSPH with a C-index of 0·849 (95%CI: 0·786–0·911). Application of rHVPG in four external prospective validation cohorts still gave excellent performance with the C-index of 0·889 (95%CI: 0·752–1·000, 0·800 (95%CI: 0·614–0·986), 0·917 (95%CI: 0·772–1·000), and 0·827 (95%CI: 0·618–1·000), respectively. Intraclass correlation coefficients for inter- and intra-observer agreement were 0·92–0·99 and 0·97–0·99, respectively. A radiomics signature was developed and prospectively validated as an accurate method for noninvasive detection of CSPH in cirrhosis. The tool of rHVPG assessment can facilitate the identification of CSPH rapidly when invasive transjugular procedure is not available.

This study aimed to test how different QT interval variability (QTV) indices change in patients with coronary artery disease (CAD) and congestive heart failure (CHF). Twenty-nine healthy volunteers, 29 age-matched CAD patients, and 20 age-matched CHF patients were studied. QT time series were derived from 5-min resting lead-II electrocardiogram (ECG). Time domain indices [mean, SD, and QT variability index (QTVI)], frequency-domain indices (LF and HF), and nonlinear indices [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns] were calculated. In order to account for possible influence of heart rate (HR) on QTV, all the calculations except QTVI were repeated on HR-corrected QT time series (QTc) using three correction methods (i.e., Bazett, Fridericia, and Framingham method). Results showed that CHF patients exhibited increased mean, increased SD, increased LF and HF, decreased T-wave amplitude, increased QTVI, and decreased PE, while showed no significant changes in SampEn. Interestingly, CHF patients also showed significantly changed distribution of the dynamical patterns with less monotonously changing patterns while more fluctuated patterns. In CAD group, only QTVI was found significantly increased as compared with healthy controls. Results after HR correction were in common with those before HR correction except for QTc based on Bazett correction. Graphical abstract Fig. The framework of this paper. The arrows show the sequential analysis of the data.

Molecular diagnostics that involve nucleic acid amplification tests (NAATs) are crucial for prevention and treatment of infectious diseases. In this study, we developed a simple, inexpensive, disposable, fully 3D printed microfluidic reactor array that is capable of carrying out extraction, concentration and isothermal amplification of nucleic acids in variety of body fluids. The method allows rapid molecular diagnostic tests for infectious diseases at point of care. A simple leak-proof polymerization strategy was developed to integrate flow-through nucleic acid isolation membranes into microfluidic devices, yielding a multifunctional diagnostic platform. Static coating technology was adopted to improve the biocompatibility of our 3D printed device. We demonstrated the suitability of our device for both end-point colorimetric qualitative detection and real-time fluorescence quantitative detection. We applied our diagnostic device to detection of Plasmodium falciparum in plasma samples and Neisseria meningitides in cerebrospinal fluid (CSF) samples by loop-mediated, isothermal amplification (LAMP) within 50 minutes. The detection limits were 100 fg for P. falciparum and 50 colony-forming unit (CFU) for N. meningitidis per reaction, which are comparable to that of benchtop instruments. This rapid and inexpensive 3D printed device has great potential for point-of-care molecular diagnosis of infectious disease in resource-limited settings.

Rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for early diagnostics and timely medical treatment of coronavirus disease 2019 (COVID-19). However, current detection methods typically rely on expensive and bulky instrumentation. Here, we developed a simple, sensitive, instrument-free, CRISPR-based diagnostics of SARS-CoV-2 using a self-contained microfluidic system. The microfluidic chip integrates isothermal amplification, CRISPR cleavage, and lateral flow detection in a single, closed microfluidic platform, enabling contamination-free, visual detection. To simplify the operation and transportation of the device, we lyophilized the CRISPR reagents in the reaction chamber and pre-stored the liquid solutions in blisters. We employed a low-cost, portable hand warmer to incubate the microfluidic chip without the need for electricity. The self-contained microfluidic system can detect down to 100 copies of SARS-CoV-2 RNA. Further, we clinically validated our method by detecting 24 COVID-19 clinical nasopharyngeal swab samples, achieving excellent sensitivity (94.1%), specificity (100%), and accuracy (95.8%). This simple, sensitive, and affordable microfluidic system represents a promising tool for point-of-care diagnostics of COVID-19 and other infectious diseases., Graphical abstract Image 1

To enable inexpensive molecular detection at the point-of-care and at home with minimal or no instrumentation, it is necessary to streamline unit operations and store reagents refrigeration-free. To address this need, a multifunctional enzymatic amplification reactor that combines solid-phase nucleic acid extraction, concentration, and purification; refrigeration-free storage of reagents with just-in-time release; and enzymatic amplification is designed, prototyped, and tested. A nucleic acid isolation membrane is placed at the reactor's inlet, and paraffin-encapsulated reagents are prestored within the reactor. When a sample mixed with chaotropic agents is filtered through the nucleic acid isolation membrane, the membrane binds nucleic acids from the sample. Importantly, the sample volume is decoupled from the reaction volume, enabling the use of relatively large sample volumes for high sensitivity. When the amplification reactor's temperature increases to its operating level, the paraffin encapsulating the reagents melts and moves out of the way. The reagents are hydrated, just-in-time, and the polymerase reaction proceeds. The amplification process can be monitored, in real-time. We demonstrate our reactors' ability to amplify both DNA and RNA targets using polymerase with both reverse-transcriptase and strand displacement activities to obtain sensitivities on-par with benchtop equipment and a shelf life exceeding 6 months.

Quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples is crucial for early diagnosis and timely medical treatment of coronavirus disease 2019. Here, we describe a digital warm-start CRISPR (dWS-CRISPR) assay for sensitive quantitative detection of SARS-CoV-2 in clinical samples. The dWS-CRISPR assay is initiated at above 50 °C and overcomes undesired premature target amplification at room temperature, enabling accurate and reliable digital quantification of SARS-CoV-2. By targeting SARS-CoV-2's nucleoprotein gene, the dWS-CRISPR assay is able to detect down to 5 copies/μl SARS-CoV-2 RNA in the chip. It is clinically validated by quantitatively determining 32 clinical swab samples and three clinical saliva samples. Moreover, it has been demonstrated to directly detect SARS-CoV-2 in heat-treated saliva samples without RNA extraction. Thus, the dWS-CRISPR method, as a sensitive and reliable CRISPR assay, facilitates accurate SARS-CoV-2 detection toward digitized quantification., Graphical abstract A digital warm-start CRISPR (dWS-CRISPR) assay for sensitive, quantitative detection of SARS-CoV-2 in clinical samples.Image 1

Background and objective: Recent advances in deep learning have been applied to ECG detection and obtained great success. The spatial and temporal information from ECG signals is fused by combining convolutional neural networks (CNN) with recurrent neural network (RNN). However, these networks ignore the different contribution of local and global segments of a feature map extracted from the ECG and the correlation relationship between the above two segments. To address this issue, a novel convolutional neural network with non-local convolutional block attention module(NCBAM) is proposed to automatically classify ECG heartbeats. Methods: Our proposed method consists of a 33-layer CNN architecture followed by a NCBAM module. Initially, preprocessed electrocardiogram (ECG) signals are fed into the CNN architecture to extract the spatial and channel features. Further, long-range dependencies of representative features along spatial and channel axis are captured by non-local attention. Finally, the spatial, channel and temporal information of ECG are fused by a learned matrix. The learned matrix is to mine rich relationship information across the above three types of information to make up for the different contribution. Results and conclusion: The proposed method achieves an average F 1 score of 0.9664 on MIT-BIH arrhythmia database, as well as A U C of 0.9314 and F max of 0.8507 on PTB-XL ECG database. Compared with the state-of-the-art attention mechanism based on the same public database, NCBAM achieves an obvious improvement in classifying ECG heartbeats. The results demonstrate the proposed method is reliable and efficient for ECG beat classification.

BACKGROUND The wide array of pathogens responsible for infectious diseases makes it difficult to identify causative pathogens with single-plex tests. Although multiplex PCR detects multiple targets, it is restricted to centralized laboratories, which delays test results or makes multiplexing unavailable, depriving healthcare providers of critical, real-time information. METHODS To address the need for point-of-care (POC) highly multiplexed tests, we propose the 2-stage, nested-like, rapid ( RESULTS In experiments on a benchtop and in a microfluidic format, RAMP demonstrated high level of multiplexing (≥16); high sensitivity (i.e., 1 plaque-forming unit of Zika virus) and specificity (no false positives or negatives); speed ( CONCLUSIONS RAMP is a hybrid, 2-stage, rapid, and highly sensitive and specific assay with extensive multiplexing capabilities, combining the advantages of RPA and LAMP, while circumventing their respective shortcomings. RAMP can be used in the lab, but one of its distinct advantages is amenability to simple implementation in a microfluidic format for use at the POC, providing healthcare personnel with an inexpensive, highly sensitive tool to detect multiple pathogens in a single sample, on site.

To evaluate noninvasively the severity of esophageal varices (EV) in cirrhotic patients using splenic hemodynamics obtained with dual-energy CT. We retrospectively analyzed 72 cirrhotic patients with EV between December 2018 and June 2019. Patients were divided into three groups: mild (EV1), medium (EV2), or severe (EV3) EV groups based on severity of EV assessed by endoscopy. An additional control group included 20 patients with normal liver CT. All patients underwent contrast-enhanced dual-energy CT. The iodine weight in spleen (IW-S) was calculated as IW-S = IC-S (iodine concentration in spleen) × V-S (spleen volume). Differences between EV and control groups were analyzed using one-way analysis of variance with Welch’s correction. Games-Howell test made further pairwise comparison. The diagnostic value of IW-S on high-risk EV (EV2, EV3, or EV1 with red color sign) was evaluated using the ROC curve. p

Background The significant association of myocardial ischemia with elevated QT interval variability (QTV) has been reported in myocardial infarction (MI) patients. However, the influence of the time course of MI on QTV has not been investigated systematically. Method Short-term QT and RR interval time series were constructed from the 5 min electrocardiograms of 49 coronary patients without MI and 26 patients with old MI (OMI). The QTV, heart rate variability (HRV), and QT–RR coupling of the two groups were analyzed using various time series analysis tools in the time- and frequency-domains, as well as nonlinear dynamics. Results Nearly all of the tested QTV indices for coronary patients with OMI were higher than those for patients without MI. However, no significant differences were found between the two groups in any of the variables employed to assess the HRV and QT–RR coupling. All of the markers that showed statistical significances in univariate analyses still possessed the capabilities of distinguishing between the two groups even after adjusting for studied baseline characteristics, including the coronary atherosclerotic burden. Conclusions The results suggested that the QTV increased in coronary patients with OMI compared to those without MI, which might reflect the influence of post-MI remodeling on the beat-to-beat temporal variability of ventricular repolarization. The non-significant differences in the HRV and QT–RR couplings could indicate that there were no differences in the modulation of the autonomic nervous system and interaction of QT with the RR intervals between the two groups.

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Background & Aims Noninvasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We investigated the ability of deep convolutional neural network (CNN) analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH. Methods We collected liver and spleen images from patients who underwent contrast-enhanced CT or MR analysis within 14 days of transjugular catheterization for hepatic venous pressure gradient measurement. The CT cohort comprised participants with cirrhosis in the CHESS1701 study, performed at 4 university hospitals in China from August 2016 through September 2017. The MR cohort comprised participants with cirrhosis in the CHESS1802 study, performed at 8 university hospitals in China and 1 in Turkey from December 2018 through April 2019. Patients with CSPH were identified as those with a hepatic venous pressure gradient of 10 mm Hg or higher. In total, we analyzed 10,014 liver images and 899 spleen images collected from 679 participants who underwent CT analysis, and 45,554 liver and spleen images from 271 participants who underwent MR analysis. For each cohort, participants were shuffled and then sampled randomly and equiprobably for 6 times into training, validation, and test data sets (ratio, 3:1:1). Therefore, a total of 6 deep CNN models for each cohort were developed for identification of CSPH. Results The CT-based CNN analysis identified patients with CSPH with an area under the receiver operating characteristic curve (AUC) value of 0.998 in the training set (95% CI, 0.996–1.000), an AUC of 0.912 in the validation set (95% CI, 0.854–0.971), and an AUC of 0.933 (95% CI, 0.883–0.984) in the test data sets. The MR-based CNN analysis identified patients with CSPH with an AUC of 1.000 in the training set (95% CI, 0.999–1.000), an AUC of 0.924 in the validation set (95% CI, 0.833–1.000), and an AUC of 0.940 in the test data set (95% CI, 0.880–0.999). When the model development procedures were repeated 6 times, AUC values for all CNN analyses were 0.888 or greater, with no significant differences between rounds (P > .05). Conclusions We developed a deep CNN to analyze CT or MR images of liver and spleen from patients with cirrhosis that identifies patients with CSPH with an AUC value of 0.9. This provides a noninvasive and rapid method for detection of CSPH ( ClincialTrials.gov numbers: NCT03138915 and NCT03766880 ).

To meet stringent limit-of-detection specifications for low abundance target molecules, a relatively large volume of plasma is needed for many blood-based clinical diagnostics. Conventional centrifugation methods for plasma separation are not suitable for on-site testing or bedside diagnostics. Here, we report a simple, yet high-efficiency, clamshell-style, superhydrophobic plasma separator that is capable of separating a relatively large volume of plasma from several hundred microliters of whole blood (finger-prick blood volume). The plasma separator consists of a superhydrophobic top cover with a separation membrane and a superhydrophobic bottom substrate. Unlike previously reported membrane-based plasma separators, the separation membrane in our device is positioned at the top of the sandwiched whole blood film to increase the membrane separation capacity and plasma yield. In addition, the device’s superhydrophobic characteristics (i) facilitates the formation of well-defined, contracted, thin blood film with a high contact angle; (ii) minimizes biomolecular adhesion to surfaces; (iii) increases blood clotting time; and (iv) reduces blood cell hemolysis. The device demonstrated a “blood in-plasma out” capability, consistently extracting 65±21.5 μL of plasma from 200 μL of whole blood in less than 10 min without electrical power. The device was used to separate plasma from Schistosoma mansoni genomic DNA-spiked whole blood with a recovery efficiency of > 84.5 ± 25.8 %. The S. mansoni genomic DNA in the separated plasma was successfully tested on our custom-made microfluidic chip by using loop mediated isothermal amplification (LAMP) method.

Rapid and quantitative molecular diagnostics in the field, at home, and at remote clinics is essential for evidence-based disease management, control, and prevention. Conventional molecular diagnostics requires extensive sample preparation, relatively sophisticated instruments, and trained personnel, restricting its use to centralized laboratories. To overcome these limitations, we designed a simple, inexpensive, hand-held, smartphone-based mobile detection platform, dubbed “smart-connected cup” (SCC), for rapid, connected, and quantitative molecular diagnostics. Our platform combines bioluminescent assay in real-time and loop-mediated isothermal amplification (BART-LAMP) technology with smartphone-based detection, eliminating the need for an excitation source and optical filters that are essential in fluorescent-based detection. The incubation heating for the isothermal amplification is provided, electricity-free, with an exothermic chemical reaction, and incubation temperature is regulated with a phase change material. A custom Android App was developed for bioluminescent signal monitoring and analysis, target quantification, data sharing, and spatiotemporal mapping of disease. SCC’s utility is demonstrated by quantitative detection of Zika virus (ZIKV) in urine and saliva and HIV in blood within 45 min. We demonstrate SCC’s connectivity for disease spatiotemporal mapping with a custom-designed website. Such a smart- and connected-diagnostic system does not require any lab facilities and is suitable for use at home, in the field, in the clinic, and particularly in resource-limited settings in the context of Internet of Medical Things (IoMT).

Pulse wave transit time (PWTT) has been widely used as an index in assessing arterial stiffness. Percutaneous coronary intervention (PCI) is usually applied to the treatment of coronary artery disease (CAD). Research on the changes in PWTT caused by PCI is helpful for understanding the impact of the PCI procedure. In addition, effects of stent sites and access sites on the changes in PWTT have not been explored. Consequently, this study aimed to provide this information. The results showed that PWTT significantly increased after PCI (p p = 0.60) between before and after PCI. Significantly increased PWTT was found in the radial access group (p p > 0.4). Additionally, PWTT in the left anterior descending (LAD) group significantly increased after PCI (p p > 0.1). Our study indicates that arterial elasticity and left ventricular functions can benefit from a successful PCI procedure, and the increase of peripheral PWTT after PCI can help to better understand the effectiveness of the procedure.

The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta’s index (PI), Guzik’s index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of

Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.

Heart rate variability (HRV) has been widely used in clinical research to provide an insight into the autonomic control of the cardiovascular system. Measurement of HRV is generally performed under a relaxed resting state. The effects of other conditions on HRV measurement, such as running, mountaineering, head-up tilt, etc, have also been investigated. This study aimed to explore whether an inflation-and-deflation process applied to a unilateral upper arm cuff would influence the HRV measurement. Fifty healthy young volunteers aged between 21 and 30 were enrolled in this study. Electrocardiogram (ECG) signals were recorded for each subject over a five minute resting state followed by a five minute external-cuff-inflation-and-deflation state (ECID state). A one minute gap was scheduled between the two measurements. Consecutive RR intervals in the ECG were extracted automatically to form the HRV data for each of the two states. Time domain (SDNN, RMSSD and PNN50), frequency domain (LFn, HFn and LF/HF) and nonlinear (VLI, VAI and SampEn) HRV indices were analyzed and compared between the two states. In addition, the effects of mean artery pressure (MAP) and heart rate (HR) on the aforementioned HRV indices were assessed for the two states, respectively, by Pearson correlation analysis. The results showed no significant difference in all aforementioned HRV indices between the resting and the ECID states (all p 0.05). The corresponding HRV indices had significant positive correlation (all p 0.01) between the two states. None of the indices showed MAP-related change (all p 0.05) for either state. Besides, none of the indices showed HR-related change (all p 0.05) for either state except the index of VLI in the resting state. To conclude, this pilot study suggested that the applied ECID process hardly influenced those commonly used HRV indices. It would thus be applicable to simultaneously measure both blood pressure and HRV indices in clinical practice.

Diastolic time (DT) may reflect the functioning status of cardiac relaxation/diastolic filling. Previous studies shown that beat-to-beat variation in RR interval (i.e., HRV) is preferentially expressed in the variation in DT series, raising up a question that whether DT possesses intrinsic variation, or whether the DT variation is simply a surrogate of HRV without additional information. In this study, we defined the diastolic phase percentages (DP) by correcting DT by the corresponding RR interval and proposed to analyze DP variation (DPV) in order to eliminate the masking effect of HRV. We studied DPV of 60 patients with congestive heart failure (CHF) and 60 healthy control subjects. Radial artery pressure (RAP) was monitored in synchrony with electrocardiography (ECG) for 5 min in order to extract the proposed DPV. Additionally, DPV was corrected by the individual mean DP level which resulted in the coefficient of DPV (cDPV). The differences in DPV and cDPV between the two groups were determined by linear regression models controlled for age and sex. Results showed that both DPV and cDPV increased significantly in CHF patients compared with healthy control subjects (both p < 0.05). Those results, highly endorsing the existence of intrinsic physiological variation in DT, may suggest that CHF patients have less stable cardiac relaxation (that is not due to HRV) or/and less stable contractility (since the diastolic and systolic phases are mutually complementary). This study may provide helpful reference for the noninvasive evaluation of cardiac functionality and for the further understanding of multiple physiological variabilities.

Molecular detection determines the presence and quantity of selected nucleic acids associated with pathogens, such as viruses, bacteria, and parasites, as well as a host’s mutant alleles in clinica...

Background Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series. Method An area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta’s index (PI), Guzik’s index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from >3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG. Results For long-term data, SI showed acceptable performance for both tasks, i.e., for task i p 0.05, d

The HIV pandemic affects 36.7 million people worldwide, predominantly in resource-poor settings. Nucleic acid-based molecular detection of HIV plays a significant role in antiretroviral treatment monitoring for HIV patients, as well as diagnosis of HIV infection in infants. Currently available molecular diagnostic methods are complex, time-consuming and relatively expensive, thus limiting their use in resource-poor settings. Recent advances in microfluidics technology have made possible low-cost integrated miniaturized devices for molecular detection and quantification of HIV at the point of care. We review recent technical advances in molecular testing of HIV using microfluidic technology, with a focus on assays based on isothermal nucleic acid amplification. Microfluidic components for sample preparation, isothermal amplification and result detection are discussed and compared. We also discuss the challenges and future directions for developing an integrated “sample-to-result” microfluidic platform for HIV molecular detection.

Complexity of heartbeat interval series is typically measured by entropy. Recent studies have found that sample entropy (SampEn) or fuzzy entropy (FuzzyEn) quantifies essentially the randomness, which may not be uniformly identical to complexity. Additionally, these entropy measures are heavily dependent on the predetermined parameters and confined to data length. Aiming at improving the robustness of complexity assessment for short-term RR interval series, this study developed a novel measure—distribution entropy (DistEn). The DistEn took full advantage of the inherent information underlying the vector-to-vector distances in the state space by probability density estimation. Performances of DistEn were examined by theoretical data and experimental short-term RR interval series. Results showed that DistEn correctly ranked the complexity of simulated chaotic series and Gaussian noise series. The DistEn had relatively lower sensitivity to the predetermined parameters and showed stability even for quantifying the complexity of extremely short series. Analysis further showed that the DistEn indicated the loss of complexity in both healthy aging and heart failure patients (both p

Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively.

Often, high-sensitivity, point-of-care (POC) clinical tests, such as HIV viral load, require large volumes of plasma. Although centrifuges are ubiquitously used in clinical laboratories to separate plasma from whole blood, centrifugation is generally inappropriate for on-site testing. Suitable alternatives are not readily available to separate the relatively large volumes of plasma from milliliters of blood that may be needed to meet stringent limit-of-detection specifications for low-abundance target molecules. We report on a simple-to-use, low-cost, pump-free, membrane-based, sedimentation-assisted plasma separator capable of separating a relatively large volume of plasma from undiluted whole blood within minutes. This plasma separator consists of an asymmetric, porous, polysulfone membrane housed in a disposable chamber. The separation process takes advantage of both gravitational sedimentation of blood cells and size exclusion-based filtration. The plasma separator demonstrated a "blood in-plasma out" capability, consistently extracting 275 ± 33.5 μL of plasma from 1.8 mL of undiluted whole blood within less than 7 min. The device was used to separate plasma laden with HIV viruses from HIV virus-spiked whole blood with recovery efficiencies of 95.5% ± 3.5%, 88.0% ± 9.5%, and 81.5% ± 12.1% for viral loads of 35,000, 3500, and 350 copies/mL, respectively. The separation process is self-terminating to prevent excessive hemolysis. The HIV-laden plasma was then injected into our custom-made microfluidic chip for nucleic acid testing and was successfully subjected to reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP), demonstrating that the plasma is sufficiently pure to support high-efficiency nucleic acid amplification.

This paper proposed a new entropy measure, Fuzzy Measure Entropy (FuzzyMEn), for the analysis of heart rate variability (HRV) signals. FuzzyMEn was calculated based on the fuzzy set theory and improved the poor statistical stability in the approximate entropy (ApEn) and sample entropy (SampEn). The simulation results also demonstrated that the FuzzyMEn had better algorithm discrimination ability when compared with the recently published fuzzy entropy (FuzzyEn), The validity of FuzzyMEn was tested for clinical HRV analysis on 120 subjects (60 heart failure and 60 healthy control subjects). It is concluded that FuzzyMEn could be considered as a valid and reliable method for a clinical HRV application.

Pattern synchronization (PS) can capture one aspect of the dynamic interactions between bivariate physiological systems. It can be tested by several entropy-based measures, e.g., cross sample entropy (X-SampEn), cross fuzzy entropy (X-FuzzyEn), multivariate multiscale entropy (MMSE), etc. A comprehensive comparison on their distinguishability is currently missing. Besides, they are highly dependent on several pre-defined parameters, the threshold value r in particular. Thus, their consistency also needs further elucidation. Based on the well-accepted assumption that a tight coupling necessarily leads to a high PS, we performed a couple of evaluations over several simulated coupled models in this study. All measures were compared to each other with respect to their consistency and distinguishability, which were quantified by two pre-defined criteria-degree of crossing (DoC) and degree of monotonicity (DoM). Results indicated that X-SampEn and X-FuzzyEn could only work well over coupled stochastic systems with meticulously selected r. It is thus not recommended to apply them to the intrinsic complex physiological systems. However, MMSE was suitable for both, indicating by relatively higher DoC and DoM values. Final analysis on the cardiorespiratory coupling validated our results.

Objective: In this study, we developed a joint distribution entropy (JDistEn) method to robustly estimate the coupling in short physiological series. Methods: The JDistEn method is derived from a joint distance matrix which is constructed from a combination of the distance matrix corresponding to each individual data channel using a geometric mean calculation. A coupled Rossler system and a coupled dual-kinetics neural mass model were used to examine how well JDistEn performed, specifically, its sensitivity for detecting weak coupling, its consistency in gauging coupling strength, and its reliability in processing input of decreased data length. Performance of JDistEn in estimating physiological coupling was further examined with bivariate electroencephalography data from rats and RR interval and diastolic time interval series from human beings. Cross-sample entropy (XSampEn), cross-conditional entropy (XCE), and Shannon entropy of diagonal lines in the joint recurrence plots (JENT) were applied for purposes of comparison. Results: Simulation results suggest that JDistEn showed markedly higher sensitivity than XSampEn, XCE, and JENT for dynamics in weak coupling, although as the simulation models were more intensively coupled, JDistEn performance was comparable to the three others. In addition, this improved sensitivity was much more pronounced for short datasets. Experimental results further confirmed that JDistEn outperformed XSampEn, XCE, and JENT for detecting weak coupling, especially for short physiological data. Conclusion: This study suggested that our proposed JDistEn could be useful for continuous and even real-time coupling analysis for physiological signals in clinical practice.